# -*- coding: utf-8 -*-
from datetime import timedelta
from spmi.ods.tidb.spmi_temporary_fee_bill import spmi_ods__spmi_temporary_fee_bill
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator

spmi_dwd__dwd_spmi_temporary_fee_bill_base_dt = SparkSqlOperator(
    task_id='spmi_dwd__dwd_spmi_temporary_fee_bill_base_dt',
    task_concurrency=1,
    pool_slots=4,
    master='yarn',
    name='spmi_dwd__dwd_spmi_temporary_fee_bill_base_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='spmi/dwd/tidb/dwd_spmi_temporary_fee_bill_base_dt/execute.hql',
    retries=0,
    pool='spmi_piece',
    driver_memory='24G',
    driver_cores=12,
    executor_cores=10,
    executor_memory='40G',
    email=['lukunming@jtexpress.com', 'yl_bigdata@yl-scm.com'],
    num_executors=30,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 40,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '15G',  # 堆外内存
          'spark.sql.shuffle.partitions': 800,
          'spark.shuffle.file.buffer': '64m'
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions': 20,  # 每天生成 20 个分区
              'hive.exec.max.dynamic.partitions.pernode': 20,  # 每天生成 20 个分区
              },
    yarn_queue='pro',
    execution_timeout=timedelta(hours=1),
)

spmi_dwd__dwd_spmi_temporary_fee_bill_base_dt << spmi_ods__spmi_temporary_fee_bill
